This document discusses choosing a machine learning technique to solve a problem. It begins with an overview of machine learning and popular approaches like linear regression, logistic regression, decision trees, k-means clustering, principal component analysis, support vector machines, and neural networks. It then discusses important considerations like knowing your data, cleaning your data, categorizing the problem, understanding constraints, choosing an algorithm, and evaluating models. Programming languages like Python and libraries, datasets, and cloud support resources are also mentioned.
Machine Learning and Real-World ApplicationsMachinePulse
This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.
The document provides guidance on building an end-to-end machine learning project to predict California housing prices using census data. It discusses getting real data from open data repositories, framing the problem as a supervised regression task, preparing the data through cleaning, feature engineering, and scaling, selecting and training models, and evaluating on a held-out test set. The project emphasizes best practices like setting aside test data, exploring the data for insights, using pipelines for preprocessing, and techniques like grid search, randomized search, and ensembles to fine-tune models.
The document provides an overview of machine learning. It defines machine learning as algorithms that can learn from data to optimize performance and make predictions. It discusses different types of machine learning including supervised learning (classification and regression), unsupervised learning (clustering), and reinforcement learning. Applications mentioned include speech recognition, autonomous robot control, data mining, playing games, fault detection, and clinical diagnosis. Statistical learning and probabilistic models are also introduced. Examples of machine learning problems and techniques like decision trees and naive Bayes classifiers are provided.
The document is an internship report submitted by Amit Kumar to Persistent System Limited detailing work done to classify handwritten digits using machine learning algorithms. It provides an overview of tasks completed including understanding the problem and data, building a random forest model to classify digits, and evaluating the model's performance. Multiple models were created using random samples of the training data and results were aggregated to validate the overall accuracy of the digit classification.
The Presentation answers various questions such as what is machine learning, how machine learning works, the difference between artificial intelligence, machine learning, deep learning, types of machine learning, and its applications.
This document discusses the past, present, and future of machine learning. It outlines how machine learning has evolved from early attempts at neural networks and expert systems to today's deep learning techniques powered by large datasets and distributed computing. The document argues that machine learning and predictive analytics will be core capabilities that impact many industries and applications going forward, including personalized insurance, fraud detection, equipment monitoring, and more. Intelligence from machine learning will become "ambient" and help solve hard problems by extracting value from big data.
Machine learning is a branch of artificial intelligence. In which computers study algorithms. If I say in simple terms, machine learning is a computer algorithm study method that allows computer programs to learn from their experience. Now the question arises what is the algorithm.
https://www.viewofpeoples.xyz/2020/08/What-is-machine-learning.html
Applied Artificial Intelligence Unit 3 Semester 3 MSc IT Part 2 Mumbai Univer...Madhav Mishra
The document discusses machine learning paradigms including supervised learning, unsupervised learning, clustering, artificial neural networks, and more. It then discusses how supervised machine learning works using labeled training data for tasks like classification and regression. Unsupervised learning is described as using unlabeled data to find patterns and group data. Semi-supervised learning uses some labeled and some unlabeled data. Reinforcement learning provides rewards or punishments to achieve goals. Inductive learning infers functions from examples to make predictions for new examples.
Machine Learning: Applications, Process and TechniquesRui Pedro Paiva
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Machine learning is a branch of artificial intelligence that uses statistical techniques to give computer systems the ability to "learn" with data, without being explicitly programmed. The goal of machine learning is to build programs that can teach themselves to grow and change when exposed to new data. There are supervised, unsupervised, and reinforcement learning techniques used in machine learning applications across many fields including computer vision, speech recognition, robotics, healthcare, and finance.
This document provides an overview of machine learning fundamentals and supervised learning with scikit-learn. It defines machine learning and discusses when it is appropriate to use compared to traditional programming. It also describes the different types of learning problems including supervised, unsupervised, semi-supervised and reinforcement learning. For supervised learning, it covers classification and regression problems as well as common applications. It then outlines the typical machine learning pipeline including data collection, preparation, model training, evaluation and addresses issues like overfitting and underfitting.
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Note: This is an old slide deck. The content on building internal ML platforms is a bit outdated and slides on the model choices do not include deep learning models.
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Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data.
Applied Artificial Intelligence Unit 4 Semester 3 MSc IT Part 2 Mumbai Univer...Madhav Mishra
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- Specific concepts discussed in more depth include genetic algorithms, genetic programming, swarm intelligence, ant colony optimization, and metaheuristics.
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The document discusses machine learning and data science concepts. It begins with an introduction to machine learning and the machine learning process. It then provides an overview of select machine learning algorithms and concepts like bias/variance, generalization, underfitting and overfitting. It also discusses ensemble methods. The document then shifts to discussing time series, functions for manipulating time series, and laying the foundation for time series prediction and forecasting. It provides examples of applying techniques like median filtering to smooth time series data. Overall, the document provides a high-level introduction and overview of key machine learning and time series concepts.
Lesson 1 - Overview of Machine Learning and Data Analysis.pptxcloudserviceuit
This document provides an overview of machine learning and data analysis. It defines machine learning as a field of artificial intelligence that enables computers to learn from data without being explicitly programmed. The main types of machine learning are supervised, unsupervised, and reinforcement learning. Data analysis is the process of extracting meaningful insights from data through techniques like cleaning, exploring for patterns/trends, statistical analysis, and visualization. Machine learning automates many data analysis tasks and can be applied through techniques like classification, clustering, and regression. The relationship between machine learning and data analysis fuels discovery, with data analysis providing foundation and machine learning generating insights.
It's Machine Learning Basics -- For You!To Sum It Up
Machine learning is a branch of artificial intelligence that uses data and algorithms to enable computers to learn and improve at tasks without being explicitly programmed. There are three main types of machine learning: supervised learning which uses labeled training data, unsupervised learning which finds patterns in unlabeled data, and reinforcement learning where an agent learns from trial-and-error interactions with an environment. Machine learning is important because it allows automation through data-driven pattern recognition, enables personalization at scale, and accelerates scientific discovery through analysis of massive and complex datasets.
This document provides an introduction to machine learning, including definitions, types of machine learning problems, common algorithms, and typical machine learning processes. It defines machine learning as a type of artificial intelligence that enables computers to learn without being explicitly programmed. The three main types of machine learning problems are supervised learning (classification and regression), unsupervised learning (clustering and association), and reinforcement learning. Common machine learning algorithms and examples of their applications are also discussed. The document concludes with an overview of typical machine learning processes such as selecting and preparing data, developing and evaluating models, and interpreting results.
Machine learning is a type of artificial intelligence that allows systems to learn from data without being explicitly programmed. The document provides an introduction to machine learning, explaining what it is, why it is used, common algorithms, advantages, and challenges. Some key challenges discussed include poor quality data, overfitting or underfitting training data, the complexity of machine learning processes, lack of training data, slow implementation speeds, and imperfections in algorithms as data grows.
AI-900 - Fundamental Principles of ML.pptxkprasad8
Automated machine learning uses algorithms to automate the machine learning workflow including data preprocessing, model selection, hyperparameter tuning, and evaluation to build an optimal machine learning model with little or no human involvement. It can save time by automating repetitive tasks and help identify the best performing models for various types of machine learning problems like classification, regression, and clustering. Automated machine learning tools provide an end-to-end experience to build, deploy, and manage machine learning models at scale with minimal coding or machine learning expertise required.
This document provides an introduction to data science. It discusses the different types of data including traditional structured data, big unstructured data, and semi-structured data. It also summarizes the key differences between analysis and analytics, qualitative and quantitative analytics, business intelligence, machine learning, and traditional data science methods. Common data science tools and job positions are also outlined.
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نقيب المبرمجين بالدقهلية
بعنوان
"IT INDUSTRY"
How To Getting Into IT With Zero Experience
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و الحضور من تطبيق زووم
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Sample Codes: https://github.com/davegautam/dotnetconfsamplecodes
Presentation on How you can get started with ML.NET. If you are existing .NET Stack Developer and Wanna use the same technology into Machine Learning, this slide focuses on how you can use ML.NET for Machine Learning.
Recently, in the fields Business Intelligence and Data Management, everybody is talking about data science, machine learning, predictive analytics and many other “clever” terms with promises to turn your data into gold. In this slides, we present the big picture of data science and machine learning. First, we define the context for data mining from BI perspective, and try to clarify various buzzwords in this field. Then we give an overview of the machine learning paradigms. After that, we are going to discuss - at a high level - the various data mining tasks, techniques and applications. Next, we will have a quick tour through the Knowledge Discovery Process. Screenshots from demos will be shown, and finally we conclude with some takeaway points.
Data mining involves finding hidden patterns in large datasets. It differs from traditional data access in that the query may be unclear, the data has been preprocessed, and the output is an analysis rather than a data subset. Data mining algorithms attempt to fit models to the data by examining attributes, criteria for preference of one model over others, and search techniques. Common data mining tasks include classification, regression, clustering, association rule learning, and prediction.
Look no further than our comprehensive Data Science Training program in Chandigarh. Designed to equip individuals with the skills and knowledge required to thrive in today's data-centric world, our course offers a unique blend of theoretical foundations and hands-on practical experience.
The slide has details on below points:
1. Introduction to Machine Learning
2. What are the challenges in acceptance of Machine Learning in Banks
3. How to overcome the challenges in adoption of Machine Learning in Banks
4. How to find new use cases of Machine Learning
5. Few current interesting use cases of Machine Learning
Please contact me (shekup@gmail.com) or connect with me on LinkedIn (https://www.linkedin.com/in/shekup/) for more explanation on ML and how it may help your business.
The slides are inspired by:
Survey & interviews done by me with Bankers & Technology Professionals
Presentation from Google NEXT 2017
Presentation by DATUM on Youtube
Royal Society Machine Learning
Big Data & Social Analytics Course from MIT & GetSmarter
This document provides an overview of data science tools, techniques, and applications. It begins by defining data science and explaining why it is an important and in-demand field. Examples of applications in healthcare, marketing, and logistics are given. Common computational tools for data science like RapidMiner, WEKA, R, Python, and Rattle are described. Techniques like regression, classification, clustering, recommendation, association rules, outlier detection, and prediction are explained along with examples of how they are used. The advantages of using computational tools to analyze data are highlighted.
"The proposed system overcomes the above mentioned issue in an efficient way. It aims at analyzing the number of fraud transactions that are present in the dataset.
"
The only way our model can perform at its best if it understands our data the best. Most algorithms only understand numeric data but in practical life that's impossible for us to have every feature in numeric form. This presentation will take you all through various techniques by which various types of features can be handled.
The document discusses machine learning, providing definitions and examples. It outlines the history and development of machine learning, describes common applications like image and speech recognition. It also covers different types of machine learning including supervised, unsupervised, and reinforcement learning. Challenges in machine learning like data quality issues and overfitting/underfitting are addressed. Popular programming languages for machine learning like Python, Java, C/C++ are also listed.
Roger S. Barga discusses his experience in data science and predictive analytics projects across multiple industries. He provides examples of predictive models built for customer segmentation, predictive maintenance, customer targeting, and network intrusion prevention. Barga also outlines a sample predictive analytics project for a real estate client to predict whether they can charge above or below market rates. The presentation emphasizes best practices for building predictive models such as starting small, leveraging third-party tools, and focusing on proxy metrics that drive business outcomes.
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org. If you would like to use this material to further our mission of improving access to machine learning. Education please reach out to inquiry@deltanalytics.org.
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IT market in Israel, economic background, forecasts of 160 categories and the infrastructure and software products in those categories, professional services also. 710 vendors are ranked in 160 categories.
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4. Our chat today :)
• What is Machine Learning?
• What are some popular approaches?
• How to think?
• How to build your model?
• What are the resources?
• Let’s get to action.
5. Artificial Intelligence
• Anything which is not natural and created by humans is artificial.
• Intelligence means ability to understand, reason, plan, adapt, etc.
• So any code, tech or algorithm that enable machine to mimic,
develop or demonstrate the human cognition or behavior is AI
11. So what is Machine Learning?
• Machine learning is the field of study
that gives computers the ability to
learn without being explicitly
programmed.
• In simple term, Machine Learning
means making prediction based on
data.
12. The ML Mindset
“ML changes the way you think about a problem. The focus shifts
from a mathematical science to a natural science, running
experiments and using statistics, not logic, to analyze its results."
– Peter Norvig
Google Research Director
15. The ML Mindset
F(a,b) = a + b
a = 1; b = 2;
3
F(a,b) = x*a + y*b
a = 1; b = 2;
3
x = ?; y = ?
16. The steps
• Machine learning is part art and part science. :)
• There is no one solution or one approach that fits all.
• There are several factors that can affect your decision to choose a
machine learning algorithm.
18. Know your data
Before you start looking at different ML algorithms, you need to have a
clear picture of your data, your problem and your constraints.
19. Know your data
Before you start looking at different ML algorithms, you need to have a
clear picture of your data, your problem and your constraints.
20. Know your data
1. Look at Summary statistics and visualizations
• Percentages can help identify the range for most of the data
• Averages and medians can describe central tendency
• Correlations can indicate strong relationships
2. Visualize the data
• Box plots can identify outliers
• Density plots and histograms show the spread of data
• Scatter plots can describe bivariate relationships
21. Since the collected data may be in an undesired format, unorganized,
or extremely large, further steps are needed to enhance its quality. The
three common steps for preprocessing data are formatting, cleaning,
and sampling.
Clean your data
22. 1. How do I deal with missing value?
• Missing data affects some models more than others.
• Even for models that handle missing data, they can be sensitive to it (missing data
for certain variables can result in poor predictions)
2. Does the data needs to be aggregated?
Clean your data
25. 3. What do I do with outliers?
• Outliers can be very common in multidimensional data.
• Some models are less sensitive to outliers than others.
Usually tree models are less sensitive to the presence of
outliers. However regression models, or any model that
tries to use equations, could definitely be effected by
outliers
• Outliers can be the result of bad data collection, or they can
be legitimate extreme values.
Clean your data
27. Augment your data
1. Feature engineering is the process of going from raw
data to data that is ready for modeling. It can serve
multiple purposes:
• Make the models easier to interpret (e.g. binning)
• Capture more complex relationships (e.g. NNs)
• Reduce data redundancy and dimensionality (e.g. PCA)
• Rescale variables (e.g. standardizing or normalizing)
2. Different models may have different feature engineering
requirements.
28. Categorize the problem
1. Categorize by input:
• If you have labelled data, it’s a supervised learning problem.
• If you have unlabelled data and want to find structure, it’s an unsupervised
learning problem.
• If you want to optimize an objective function by interacting with an environment,
it’s a reinforcement learning problem.
2. Categorize by output
• If the output of your model is a number, it’s a regression problem.
• If the output of your model is a class, it’s a classification problem.
• If the output of your model is a set of input groups, it’s a clustering problem.
• Do you want to detect an anomaly ? That’s anomaly detection.
30. Understand your constraints
• What is your data storage capacity? Depending on the storage capacity of your
system, you might not be able to store gigabytes of classification/regression
models or gigabytes of data to clusterize. This is the case, for instance, for
embedded systems.
• Does the prediction have to be fast? In real time applications, it is obviously very
important to have a prediction as fast as possible. For instance, in autonomous
driving, it’s important that the classification of road signs be as fast as possible to
avoid accidents.
• Does the learning have to be fast? In some circumstances, training models quickly
is necessary: sometimes, you need to rapidly update, on the fly, your model with a
different dataset.
31. Choose the algorithm
Identify the algorithms that are applicable and practical to implement using the tools
at your disposal.
Some of the factors affecting the choice of a model are:
• Whether the model meets the business goals
• How much preprocessing the model needs
• How accurate the model is
• How explainable the model is
• How fast the model is: How long does it take to build a model, and how long does
the model take to make predictions.
• How scalable the model is
32. Take care at complexity
Making the same algorithm more complex increases the chance of overfitting.
A model is more complex when:
• It relies on more features to learn and predict (e.g. using two features vs ten
features to predict a target)
• It relies on more complex feature engineering (e.g. using polynomial terms,
interactions, or principal components)
• It has more computational overhead (e.g. a single decision tree vs. a random
forest of 100 trees).
35. Linear Regression
Regression algorithms can be used for example, when you
want to compute some continuous value as compared to
Classification where the output is categoric.
• Time to go one location to another
• Predicting sales of particular product next month
• Impact of blood alcohol content on coordination
• Predict monthly gift card sales and improve yearly
revenue projections
36. Logistic Regression
Logistic regression performs binary classification, so the
label outputs are binary. It takes linear combination of
features and applies non-linear function (sigmoid) to it, so
it’s a very small instance of neural network.
• Predicting the Customer Churn
• Credit Scoring & Fraud Detection
• Measuring the effectiveness of marketing
campaigns
37. Decision trees
Single trees are used very rarely, but in composition with
many others they build very efficient algorithms such as
Random Forest or Gradient Tree Boosting.
• Investment decisions
• Customer churn
• Banks loan defaulters
• Build vs Buy decisions
• Sales lead qualifications
38. K-means
Sometimes you don’t know any labels and your goal is to
assign labels according to the features of objects. This is
called clusterization task.
If there are questions like how is this organized or
grouping something or concentrating on particular groups
etc. in your problem statement then you should go with
Clustering.
• When there is a large group of users and you want to
divide them into particular groups based on some
common attributes.
39. Principal component analysis
Principal component analysis provides dimensionality
reduction. Sometimes you have a wide range of features,
probably highly correlated between each other, and
models can easily overfit on a huge amount of data. Then,
you can apply PCA.
40. Support Vector Machines
Support Vector Machine (SVM) is a supervised
machine learning technique that is widely used in
pattern recognition and classification problems —
when your data has exactly two classes.
• detecting persons with common diseases
such as diabetes
• hand-written character recognition
• text categorization — news articles by topics
• stock market price prediction
41. Neural networks
Neural Networks take in the weights of connections
between neurons.
Extremely complex models can be trained and they
can be utilized as a kind of black box, without playing
out an unpredictable complex feature engineering
before training the model.
Object recognition has been as of late enormously
enhanced utilizing Deep Neural Networks. Applied to
unsupervised learning tasks, such as feature
extraction, deep learning also extracts features from
raw images or speech with much less human
intervention.
46. What you need to know ?
It is mandatory to learn a programming language, preferably Python, along with the
required analytical and mathematical knowledge. You can touch on:
• Linear algebra for data analysis: Scalars, Vectors, Matrices, and Tensors
• Mathematical Analysis: Derivatives and Gradients
• Probability theory and statistics
• Multivariate Calculus
• Algorithms and Complex Optimizations
47. Programming Languages
Python is hands down the best programming language for
Machine Learning applications.
Other programming languages that could to use for Machine
Learning Applications are R, C++, JavaScript, Java, C#, Julia,
Shell, TypeScript, and Scala.